Long-term changes in organic matter and microbial properties ...

Agriculture, Ecosystems and Environment 123 (2008) 175–184 www.elsevier.com/locate/agee

Long-term changes in organic matter and microbial properties resulting from manuring practices in an arid cultivated soil in Burkina Faso G.T. Freschet a,*, D. Masse b, E. Hien c, S. Sall d, J.-L. Chotte a a

b

Institut de Recherche pour le De´veloppement (IRD), BP 6450, Montpellier, France Institut de Recherche pour le De´veloppement (IRD), BP 434, 101 Antananarivo, Madagascar c UFR SVT, Universite´ de Ouagadougou, 03 BP 7021, Ouagadougou 03, Burkina Faso d Institut de Recherche pour le De´veloppement (IRD), BP 1386, Dakar, Senegal Received 24 January 2007; received in revised form 22 May 2007; accepted 30 May 2007 Available online 23 July 2007

Abstract Fallows and livestock manure are the main means for improving the fertility of agro-pastoral systems in West African Sahel. As the general shortage of cropland has led to shorter fallow periods, manure appears to be the only means of maintaining soil productivity. Several studies have focused on the general pattern of nutrient release from manure but, so far as we are aware, few studies have investigated the long-term changes of manure input to tropical soils. This research project was carried out on ferralic arenosol in north Burkina Faso to assess the longterm residual effect of organic matter (sheep and goat manure) on land where livestock had been corralled. Microbial activities, soil organic matter and vegetal production were evaluated and a soil organic matter model was then drawn up and fitted to the data. The residual effect decreased rapidly during the first 5 years after corralling but remained significant for up to 11 years. The duration and magnitude of the residual effect depended mainly on the amount of manure applied. The differences in soil organic matter recorded between plots that had not been corralled for many years might result from the different amounts of manure applied in the past. Vegetal production and denitrification potentials were negligible below a minimum soil organic matter threshold. The initial soil organic matter level, the soil organic matter threshold and the residual effect of corralling should be taken in account when advising on the management of ecosystem services such as vegetal production and control of greenhouse gas emissions. For these purposes, it could be useful to produce a more advanced model. # 2007 Elsevier B.V. All rights reserved. Keywords: Corralling; Manure; Soil organic matter; Microbial activity; Ecosystem services; Burkina Faso

1. Introduction In the West African Sahel, agriculture and livestock farming rely on the management of organic matter. However, for various reasons, organic matter is becoming more scarce, jeopardizing the sustainability of mixedfarming smallholder systems (Manlay et al., 2004). * Corresponding author. Present address: Centre Technologic Forestal de Catalunya, Pujada del Seminari s/n, 25280 Solsona, Spain. Tel.: +34 973 481752; fax: +34 973 481392. E-mail addresses: [email protected], [email protected] (G.T. Freschet). 0167-8809/$ – see front matter # 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.agee.2007.05.012

Increasing demographic pressure combined with the loss of collective management of agricultural land has led to more extensive agriculture and increased pressure on noncultivated areas, which in turn has led to limited fallowing practice and a reduction in the availability of pasture land (Marchal, 1983). Many such cropping systems are now subject to soil mining agriculture (Van der Pol and Traore, 1993). It is, therefore, essential to find alternative techniques or to improve traditional techniques. Better integration of agriculture and livestock farming appears to be a promising policy (Ganry et al., 2001; Manlay et al., 2004). All sahelian smallholdings have livestock, which provides advantages as well as constraints for

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G.T. Freschet et al. / Agriculture, Ecosystems and Environment 123 (2008) 175–184

agricultural practices. Livestock fed on crop residues cannot balance the nutrient depletion, whereas if they also are fed on rangelands, nutrients can be transferred from the rangelands to the croplands (Powell et al., 1996). Although this will not increase nutrients at agro-pastoral scale, keeping livestock is a crucial resource for farmers and can help to maintain a balanced nutrient cycle by manure management (Erenstein, 2003). As established by Rufino et al. (2006), each step in recycling organic matter in farming systems is subject to nutrient losses. The overnight corralling technique discussed in this study makes it possible to short-cut this cycle (Ikpe and Powell, 2002). It enables urines, which represent between 40 and 60% of N contained in livestock excreta, to return to the field, as determined by a comparison of yields of crops from land fertilized with manure and land fertilized with urines (Powell and Ikpe, 1992). However, Esse et al. (2001) noted that overnight corralling led to high concentrations of manure on small areas of the field which cause nutrient losses during the rainy season. This practice has nevertheless the major advantage of reducing the labour needed to collect, store and spread manure. The residual effect of manure has been widely demonstrated (Schroder, 2005) but little is known about its determinant factors and its duration so far as tropical agrosystems are concerned. Few studies have investigated these aspects of the changes in organic matter (Mafongoya et al., 2000) and in most cases, attention was paid only to the first year (Somda and Powell, 1998) or the first 2 years after the addition of manure (Bacye´ et al., 1998). This study deals with the long-term effects of adding manure at field scale in situations where there are long intervals between corralling periods. Although practices varied considerably, the long-term effects of applying manure were established using data on soil organic matter, microbial activity and vegetal production. The study was designed to produce a general estimation of this residual effect and analyse the determinant factors by drawing up a mathematical model. The influence of the residual effect of applying manure on ecosystem services such as vegetal production and the reduction of greenhouse gases were also considered.

2. Materials and methods 2.1. The site This study was conducted in Banh (148040 N, 28260 W), a village in Lorum province, in the northern part of the Central Plateau of Burkina Faso. Banh is situated in the southern sahelian climatic zone, which has a 9-month dry season from September to May and a 3-month rainy season lasting from June to August. Climatic data from 1971 to 2000 showed a mean annual rainfall of 591 mm and a mean temperature of 28.7 8C (Ouahigouya meteorological station). The Food and

Agricultural Organisation classified the soil as ferralic arenosol (FAO, 1998). 2.2. Survey of corralling practices A survey of farmers in Banh was carried out to identify fields where livestock were corralled. Fifteen fields were selected where goats and sheep were regularly corralled overnight. The areas that had previously been corralled within these fields were identified. The areas selected had the same type of soil. A farm survey was also carried out to check that cultivation practices were uniform and to obtain comprehensive information about the history of each plot. Plots were eliminated if they were surrounded by trees or bushes, if crop residues had previously been burned or if there was a water course during the rainy season. Forty-nine corralling plots plus 22 control plots (12 cultivated plots that had not been fertilized and 10 uncultivated plots close to the field) were studied in detail. Control samples were taken from the uncultivated plots bordering on the farmed field selected, which, after the litter brought by the surrounding savannah had been removed, represented the soil before cultivation. The samples were classified according to the period since the last corralling. A distinction was drawn between cultivated controls (CC), uncultivated controls (UC), plots corralled every year (CEY), 1 year previously (C1), 2 years previously (C2) up to 11 years previously (C11). The annual use of the fields was fairly consistent. Mostly, the fields studied were used to cultivate millet (Pennisetum glaucum) during the rainy season. Crop residues were taken from the fields to feed livestock during periods when feed was scarce, in particular at the end of the dry season. During the dry season, livestock were allowed to feed on remaining crop residues and surrounding savannah areas and brought together into corralling areas every night. The corralling areas identified in our study were very restricted in area, implying very concentrated inputs. The study showed that the main factors responsible for the variation in the amount of manure added to land by corralling were: (i) the duration of corralling (1–7 months), (ii) the number and type of livestock corralled, (iii) the density of livestock inside the corral and (iv) the spreading of manure by farmers and tillage practices. When farmers removed the corrals, they spread the manure over an area that depended on the amount of manure present in the corral. They then tilled at a depth depending on the technique used (manual, donkey or camel) which affected the concentration of manure in the soil. All these factors were taken in account to estimate the minimum and maximum amounts of manure added to the plots by corralling. 2.3. Soil sampling Samples were collected during the dry season. Six samples were taken at random from 0 to 10 cm layer on each


plot. As cultivated plots still had cropping ridges about 20 cm high at the sampling period, samples were taken from the ridges which were considered to represent the 0–10 cm layer of the cultivated soil. Litter was removed from uncultivated plots before sampling. Samples from the same plot were pooled together, air-dried and sieved at 2 mm. Straw, gravel and faeces were separated from the >2 mm fraction and weighed. The faeces were then ground and returned to the 2 mm sieved sample. Samples were stored air-dried at 20 8C pending further analyses. 2.4. Organic carbon and nitrogen The total soil was analysed for C and N by dry combustion using a Fisons (Carlo Erba) Na-2000 elemental analyser. Since there were no carbonates in these soils, the soil total carbon (Ctot) was considered to be equal to the soil organic carbon (Corg) and expressed in mg C (g(soil))1. Ntot was expressed in mg N (g(soil))1. 2.5. Basal respiration and microbial biomass Soil sub-samples (30 g of sieved dry soil) were incubated at 100% of their water-holding capacity (80 ml water (g(dry soil))1) in closed flasks (120 ml) and kept in the dark at 28 8C for 7 days. During the incubation period, the respired CO2 in the flasks was analysed by direct injection into a microgas chromatograph. When the CO2 had been determined, the headspaces were flushed with fresh air. After 7 days’ incubation, soil sub-samples were used to determine the microbial biomass. Potential basal respiration (BR), expressed in mg CO2-C (g(soil))1 day1, was calculated when microbial respiration had stabilized, i.e. during the last 3 days of incubation. Microbial biomass N was determined using the fumigation-extraction method (Amato and Ladd, 1988) by measuring ninhydrin-reactive N compounds extracted from soils after 10 days of fumigation. The metabolic quotient (qCO2) is the ratio of BR to Cmic of a soil, expressed in mg CO2-C (mg(Cmic))1 day1. The microbial quotient (qCmic), which is the ratio of Cmic to Corg of a soil, is expressed in mg Cmic (g(Corg))1. 2.6. Denitrification potential Denitrification potential was determined by measuring the N2O concentration in closed flasks after 48 h of soil incubation at 28 8C in the dark. Ten grams of each dry soil sample were placed in a 60 ml flask with Chloramphenicol (2.5 g (g(dry soil))1) and humidified at 100% of their water-holding capacity (80 ml water (g(dry soil))1). After a 30 min delay to avoid a priming effect, the air in the flask was replaced by a 90% He, 10% C2H2 gas mixture to ensure anaerobic conditions and inhibit N2O-reductase (Lensi et al., 1995). Five millilitres of a solution containing glucose (1 mg C-glucose (g(dry soil))1), glutamic acid (1 mg C-glutamic acid (g(dry soil))1) and sodium nitrate

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(100 mg N-NO3 (g(dry soil))1) was added to the soil. An air sample of each flask was measured using a gas chromatograph. Denitrification potential was expressed in mg N2O-N (g(soil))1 day1. 2.7. Potential growth of millet (P. glaucum) The experiment was carried out in small pots under greenhouse conditions. Each pot contained 150 g of soil and 10 seeds. Millet seeds were sorted before being sown. There were five replicates for each soil sample. Each pot received 25 ml of water per day during the 32 days of the experiment. On day 32, the whole plants (root and above-ground) were harvested and placed in a drying oven at 65 8C. After 48 h, the total dry biomass of each pot was weighed to obtain the vegetal production, expressed in g. 2.8. Statistical analyses All statistical analyses were performed using XLSTAT software (Addinsoft#). Principal component analysis (PCA) was conducted with all measured variables (Ctot, Ntot, BR, DP, Cmic and vegetal production) and calculated variables (qCO2 and qCmic) to identify groups of time elapsed since corralling that were kept for all subsequent analyses. All the analyses of variance (ANOVA) performed included two factors: the time since the corralling and the farm from which the plot was selected. These two factors had a significant effect on all variables. Taking account of the farm from which the plot was selected allowed better discrimination of variables when determining the influence of the time since the corralling periods and decreased the influence of possible differences between farms. Variables Ctot and Cmic were transformed using the logarithm function to maintain data normality and homoscedasticity required for the ANOVA test. Similarly, variables BR, DP, Ntot, qCO2, qCmic and the co-ordinates of the PCA first axis were transformed into ranks before performing the ANOVA test. This transformation into ranks takes advantage of the properties of rank data and corrects data normality and homoscedasticity (Potvin and Roff, 1993). All differences between groups of time since corralling were tested for significance using the Newman–Keuls test (a = 0.05). Linear regression was used to test the significance of the correlations between DP and Ctot, DP and Ntot, vegetal production and Ctot, vegetal production and Ntot. 2.9. C stock model The model used in this study is a simple exponential decrease model based on two assumptions: (i) the organic C resulting from corralling practices does not interact with the native organic C of soil and (ii) the organic C input that may occur during the period after the last corralling is considered equal for all plots.

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The general equation used for modelling the change in C concentration over time was Cn = Ci exp(kn) + Cr, with Cn the C concentration in soil n years after corralling, Ci the C concentration added to the soil by corralling, k the C loss rate coefficient, n the number of years after corralling and Cr is the C concentration in soil without C input from corralling. The Cr was assimilated to the mean C concentration of all cultivated controls (CC). For the purposes of the model, CC were considered to have n = 20 years. This value was based on two observations. First, inquiries showed that selected fields had been cultivated for at least 20 years up to an undetermined time. Secondly, very little change was noticed when a higher n was used in our model. The minimum and maximum C concentrations added to the soil by corralling (Ci min and Ci max, respectively) were estimated on the basis of the farm surveys and data found in the literature. The results were used to determine realistic limits for the model and discriminate outliers. For these minimum and maximum limits, the Cr were respectively assimilated to the minimum and maximum C concentrations found for CC. The half-life of Ci was calculated as T1/2 = ln(2)/k. The exponential model was transformed into a linear model: ln(Cn Cr) = ln(Ci) kn and a linear regression was performed to test the significance of the model. However,

as Cn < Cr for some samples, ln(Cn Cr) could not be calculated and the significance of the model could not be tested.

3. Results 3.1. Effect of corralling practices on soil characteristics The first two factors identified by our principal component analysis (PCA) explained 77% of overall variability (Fig. 1). The first factor (F1) alone explained 60% of the variability. Measured variables (Ctot, Ntot, BR, DP, Cmic and vegetal production) were significantly positively correlated and contributed to 94% of the F1 axis construction. The second factor (F2) explained 17% of the variability. qCO2 was the main component. Together with the negatively correlated qCmic this accounted for 84% of the F2 axis construction. This F1 axis was a gradient of both soil organic matter and microbial activity. Fig. 1 shows plots according to their corralling characteristics. Plots CEY, C1, C2, C3 and C4 had generally higher values for the F1 axis than plots corralled 5–6 years previously (C5–6), 7–11 years previously (C7– 11), UC or CC. Using their barycentre, the main groups were ranked along the F1 gradient as follows: CEY > C1 >

Fig. 1. Principal component analysis of the variables Ctot, Ntot, BR, DP, Cmic, qCO2, qCmic and vegetal production and significant differences between groups of period of corralling according to the F1 axis co-ordinates. Variables are represented by straight lines. Circles represent the barycentre of the groups of time since corralling.

G.T. Freschet et al. / Agriculture, Ecosystems and Environment 123 (2008) 175–184 Fig. 2. Means, standard deviations and significant differences for selected soil properties according to their characteristic of corralling. The two factors used for the ANOVA test took account of the groups of times since corralling and the differences between plots.

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C2–4 > C5–6 > UC > C7–11 > CC (Fig. 1). A clear relationship was observed between the F1 gradient and the period since the time the various plots were last corralled. The more recent the manure inputs, the higher their values on the first axis. This relationship was, however, not linear and there were many exceptions. While groups of samples from very old corralling or plots that had never been corralled (white marks) were well grouped on the negative side of the F1 axis, groups of plots that had been recently corralled (black marks) were quite scattered, with some plots close to the group of plots that had not recently been corralled. The results of the variance analysis for F1 axis coordinates, shown in the inset of Fig. 1, indicated significant differences between the groups of time since corralling shown in the PCA graph. The plots of the CC group had significantly the lowest values on the F1 axis and the plots of the CEY and C1 groups had the significantly highest values. In between, the co-ordinates of the C2–4 group plots were significantly higher than those of the C7–11 and UC groups but not different from the plots of the C5–6 group. Groups C5–6, UC and C7–11 were not significantly different. A general pattern of decline could be observed from plots recently corralled to non-corralled plots for variables

representing soil organic matter and microbial activity (i.e. BR, DP, Cmic, Ntot, Ctot) (Fig. 2). The qCO2 showed the same trend except for the CC group. This group of noncorralled plots had a higher value that the C5–6 and C7–11 groups. The qCmic showed no particular trend. 3.2. Estimating the amount of manure spread on the field The amount of manure dry matter (DM) excreted inside the corral was estimated at between 6 and 67 kg DM m2 year1 (Table 1). Minimum and maximum amounts of manure added to the soil at the beginning of the cultivation season were 7 and 107 g DM (kg(soil))1 year1, respectively. Finally, Ci min and Ci max amounted to 3 and 45 g C (kg(soil))1, respectively. 3.3. Model of the C stock loss rate A simple model of soil organic matter decomposition was used to model C stock according to the number of years after corralling (Fig. 3). The non-linear regression applied to this model was used to find the best fit values of the adjusted variables: Ci = 17.32 mg (g(soil))1 and k = 0.21 year1.

Table 1 Estimation of the minimum and maximum manure inputs on plots

*The dilution factor due to the spreading was considered to be much higher when quantity of manure was important. Values were found either from results in the literature, when mentioned, or inquiries.


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Fig. 4. Behaviour of the model according to the initial C concentration (Ci) added to the soil. Cr was assumed to be the mean concentration of the cultivated controls. Carbon half-life (T1/2) = 3.3 years.

Fig. 3. Non-linear regression of soil organic carbon according to the time since the last corralling. The model used was Cn = Ci exp(kn) + Cr with Cr the C concentration in soil of cultivated controls, R2 = 0.34. The adjusted variables were found to be Ci = 17.32 mg (g(soil))1 and k = 0.21 year1. Dotted lines represent the estimated minimum and maximum limits of the model, given the initial range of realistic inputs (Ci min and Ci max) and the minimum and maximum concentrations of organic C in cultivated controls soils, respectively. Cultivated controls were considered to have n = 20.

potential were very limited. Above this critical level, denitrification potential and vegetal production potential followed Ctot and Ntot concentrations in the soil.

4. Discussion 4.1. Residual effect of corralling

The correlation coefficient was R2 = 0.34. The estimated limits, used to discriminate realistic values from outliers, led us to keep all C concentration values (white marks) except one (black mark). The general trend of this model indicated a decrease in the residual effect of C input that was more rapid within the first years after the input than in the following years (Fig. 3). This model implied that the higher the Ci, the longer the effect lasted. The half-life (T1/2) of the C concentration added to the soil by corralling (Ci) was found to be 3.3 years, independently of the initial C input (Fig. 4). 3.4. Organic matter, denitrification potential and vegetal production potential The denitrification potential and vegetal production potential were well correlated ( p < 104) with Ctot and Ntot concentrations in soil, i.e. with the organic matter content of the soils (Fig. 5). A threshold of Ctot and Ntot concentration in soil was, however, observed, below which both denitrification potential and vegetal production

Microbial activity and soil organic matter are generally well correlated in soil systems relying on organic matter inputs (Chaussod, 1996; Six et al., 2004). Organic matter inputs are the main resource for soil macro- and microfauna, thus controlling biological activity (Brown et al., 1994). Our results appear to agree with this statement and lead us to consider the first axis, largely constructed from the variables of microbial activity and size of the soil organic matter pool, as a gradient of soil organic matter and microbial activity. This gradient clearly showed a significant residual effect during the first 4 years following manure application. After 5–6 years this residual effect was less obvious and almost disappeared after 6 years. There was, however, high variability between plots with the same time since the last corralling. It seemed highly probable that the general pattern of the changes in soil organic matter and microbial activity in soil found here might hide a more complicated pattern. As initial manure concentrations in soil varied considerably from one plot to another, the hypothesis was made that the quantity of

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Fig. 5. Linear regressions of denitrification potential and vegetal production potential by total carbon and total nitrogen ( p < 104).

organic input was responsible for a large part of this variability. 4.2. Modelling the residual effect of corralling This model is an exponential function of the period since the last corralling taking account of the initial C input to the soil (Ci) and the soil C concentration in plots without manure input (Cr). With R2 = 0.34, the model was quite satisfactory considering the high variability of corralling. Models of kinetic C of added organic matter to soil generally include two or more C compartments (Thuries et al., 2001; Fang et al., 2005). A model developed by Pansu and Sidi (1987) differentiates the labile C compartment from the stable C compartment. The reduction in these two compartments was also exponential. Labile C is easily mineralizable and rapidly used by microbial biomass. As this compartment decreases very rapidly (Pansu and Sidi, 1987) its influence should not be noticeable 1 year after the manure input. Our model considered a single total C compartment and was assumed to be mainly influenced by the stable C compartment. It was assumed that, even in these soils that suffered from soil mining agriculture where farmers did not add fertilizers, the cultivated control plots still contained small C stocks. It is highly probable that there is some erratic input when livestock strays, from crop residues, root exudation or the

turnover of roots from cultivated plants. As these low inputs occur each year independently of the period since the last corralling, they help to maintain a low C stock in soil over time. These observations led to the addition of a constant Cr to our model. As in numerous other models, the C loss rate (k) was assumed to be constant. Applying our model to the C data from a 2-year in situ experiment of manure loss rate (Bacye´ et al., 1998), we obtained the same k value (k = 0.21 year1) for the second year of the experiment. This supports the k used for our model. Our model indicates that a greater input produces a greater, longer residual effect. The differences in soil organic matter recorded for plots that had not received manure for many years may then be attributed to the different amounts of manure applied in the past. However, the specific characteristics of sheep and goat manure may also produce this long-term residual effect. Unlike cattle manure, which can be rapidly degraded by termites, the presence of sheep and goat faeces was observed on some plots even 3 years after corralling. It has already been suggested that manure pellets of sheep could have a lower rate of decay than the less compact cattle manure (Brouwer and Powell, 1998). Nevertheless, the long residual effect found in our study was also clearly due to the large, concentrated inputs of manure on corralled plots, resulting from the very limited area of the corrals.


4.3. Key improvements in livestock management for better ecosystems services The general belief that denitrification potential is very low in poor tropical sandy soil was confirmed by our results. However, plots with manure inputs had much higher denitrification potentials than unmanured cultivated plots or uncultivated plots. The denitrification potential of our soils was well correlated with their C and N content. Our results also show that, if C and N concentrations in soil are below a given threshold, the denitrification potential could be considered as inexistent. In our C stock loss model, the rate at which the denitrification potential reaches this threshold depends on the initial manure input to the soil. Although spreading manure on the field does not affect the C loss rate over the whole field, it may affect the denitrification potential of the field. Concentrating the manure in a single plot should increase the denitrification potential by delaying the time needed to reach the threshold. On the other hand, spreading the manure more extensively should decrease the denitrification potential. In a study of five villages of Western Niger, Schlecht et al. (2004) noted that plots were corralled at intervals of 3–5 years, demonstrating that farmers were well aware of the residual effect of manure. In our study, this interval was much more varied, showing that this interval may in most cases depend on livestock availability, farmers usually trying to corral livestock on each part of their field before corralling a plot a second time. The results of our vegetal production experiment showed a critical level of plant growth for C and N concentrations above 6 mg C (g(soil))1 and 0.6 mg N (g(soil))1, respectively. Given the mean C soil concentration of 2.5 mg C (g(soil))1 (0.24 mg N (g(soil))1) found in our study, organic matter inputs of 1.2–2.4 kg DM m2 year1 are needed to reach this threshold, depending on the tillage depth (10 and 20 cm depth, respectively). However, more manure must be added to obtain a significant residual effect for cultivation seasons after the first one. For example, to maintain the C concentration in soils above the threshold for 3.3 years (T1/2), inputs of 2.4–4.8 kg DM m2 year1 are needed, depending on the tillage depth (10 and 20 cm depth, respectively). These values are above the minimum input managed by farmers in our study area but very far from the maximum input values. As greenhouse growth experiments are not as effective as field experiments, these values should be treated with caution and are not sufficient on their own to indicate accurate yearly inputs. Compared with other recommendations, this range of values seems high. Schlecht et al. (2004) noted that 4-year cumulative crop yields per weight of manure applied were lower for high manure inputs (2.1 kg DM m2) than yields recorded for lower rates of manure application (0.2– 0.4 kg DM m2). Other studies also recommended manure applications at rates of less than 0.28 kg DM m2 year1 (Brouwer and Powell, 1998) or 0.2–0.3 kg DM m2 year1

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(Esse et al., 2001) rather than larger but less frequent inputs of manure. Although some of our results produced information on aspects of manure application rates, this was not the aim of the study and it did not allow further discussion. Nevertheless, there are three possible reasons why the results of our study differed from other studies. First, the soil we studied may have had a lower organic matter content than soil in previous studies, although this is highly improbable considering that Brouwer and Powell (1998) found a soil organic matter content even lower than ours. Secondly, which is more probable, the vegetal production threshold found in our greenhouse experiment may not be representative of the threshold encountered for field cultivation. Finally, a phosphorus deficiency may be the reason for our apparently high vegetal production threshold. As there may be a threshold for vegetal production and denitrification potential, it seems important to take this into account when advising on organic matter input rates. If the initial soil organic matter content is above these thresholds, the effect of manure will be stronger on both vegetal production and denitrification potential. 4.4. Changes in microbial biomass and organic matter quality The microbial quotient is an estimate of the quality of soil organic matter and microbial access to nutrients (Knoepp et al., 2000). The metabolic quotient reflects the efficiency of the use of organic C by micro-organisms (Dilly and Munch, 1998; Knoepp et al., 2000). As underlined by Anderson (2003), qCO2 and qCmic should not be used separately. The qCmic is complementary to the qCO2 as it gives an indication of the stability of an ecosystem. It provides early warning of soil quality deterioration or environmental change (Anderson, 2003; Yan et al., 2003). No significant differences in qCmic were observed between the plots, which may indicate that there was little variation in soil organic matter quality with the age of the organic matter input. The organic matter of plots sampled comprised mainly faeces with crop residues, root exudation, etc., and may not have very different qualities. A lower value qCO2 suggests better microbial efficiency. The trend of qCO2 to decrease as the time since the last corralling increases may indicate the presence of microbial populations which are more efficient at using C compounds during the late stage of decay. Finally, it appears that, as organic matter quality in the soils studied did not vary greatly with the time after the organic matter, a small change in the microbial biomass quality may produce a more efficient use of organic matter in plots that were corralled a long time previously. According to Anderson and Domsch (1985), it may also indicate that the microbial biomass has recovered after disturbance and become more stable; although more recent analyses disagree with this hypothesis (Wardle and Ghani, 1995).

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5. Conclusion The residual effect of manure inputs, including total C and N, microbial biomass, basal respiration, denitrification potential and vegetal production potential, was found to decrease rapidly between the first and fifth years after corralling but was significant up to 11 years after corralling, compared with the cultivated control. The simple model giving the best fit to the data was an exponential function of the period since the last corralling that took account of the initial C input to the soil and the soil C concentration of plots without manure input. The duration and magnitude of the effect depended on the amount of manure applied initially which had to be very high to show a significant effect on soil organic matter.

Acknowledgements The authors would like to thank all those who helped with this study, in particular Marc Pansu, Didier Brunet, Henri Ferrer, Theódore Kabore´, Mustapha Sane and Moussa Barry for their contributions and Tamboura Hamadou for his helpful explanations about his village and its farming practices. Advice about statistical analyses was provided by Raphae¨l Manlay, Sylvain Coq, Jean-Baptiste Ferdy and David Mouillot. The authors are also grateful to Vincent Eschenbrenner, Didier Blavet and two anonymous reviewers for their helpful comments on the manuscript.

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